Correct:

C. Show comments: This display option allows you to show any comments or annotations that have been added to the indicator scores over time directly on the Time Series widget chart. These comments provide context for spikes, drops, or other notable events in the data.

D. Show confidence bands: This is a key feature related to forecasting in Performance Analytics. When forecasting is enabled for an indicator, the confidence bands are an option to display the shaded area on the chart, representing the statistical probability range (e.g., 90% or 95%) within which the future indicator scores are expected to fall.

E. Show target: This display option controls whether the Target line associated with the underlying indicator is visible on the Time Series chart. The target provides a visual goal or benchmark for the indicator‘s performance.

Incorrect:

A. Include elements without data in the legend: This is a display setting more typical for Breakdown widgets, especially those visualized as a list or a bar chart, where you might want to show all possible breakdown elements (e.g., all Assignment Groups) in the legend, even if they had zero score in the current time period. It is not a standard display setting for the main visualization of a Time Series widget.

B. Max colors: This setting is usually associated with Breakdown or Pivot widgets (like a Heatmap or a grouped bar chart), where you limit the number of colors (breakdown elements) displayed on the chart to prevent clutter, consolidating the rest into an “Others“ category. It does not apply directly to the primary display settings of a Time Series chart.

Correct:

C. Breakdown Source Security type: Whitelist / Element Security List Roles: hr_user This is the correct configuration to restrict Breakdown Element selection exclusively to users with the hr_user role. In ServiceNow Performance Analytics:

Setting the Breakdown Source Security type to “Whitelist“ ensures that only users with roles listed in the Element Security List can access the Breakdown Elements.

Adding hr_user to the Element Security List Roles enforces that restriction.

This configuration is explicitly covered in CAS–PA 2025 as the recommended method for role-based Breakdown access control.

Incorrect:

A. Breakdown Source Security type: Blacklist / Element Security List Roles: hr_user Incorrect. A Blacklist configuration would allow everyone except users with the listed roles to access the Breakdown Elements. This is the opposite of the intended restriction.

B. Breakdown Access Control Roles: hr_user / Default elements filter specify elements to show Incorrect. While Breakdown Access Control Roles can limit access to the Breakdown itself, they do not restrict selection of individual Breakdown Elements. The Default elements filter is used for display filtering, not security enforcement.

D. Breakdown Access Control Roles: hr_user / Default elements filter: specify elements to hide Incorrect. Similar to option B, this configuration affects visibility, not access control. It does not enforce role-based restriction on Breakdown Element selection.

Correct:

D. The Main Indicator and all Criteria Indicators have record collection enabled This is the required condition when using Snapshot to evaluate Spotlight records in ServiceNow Performance Analytics. Snapshot relies on record-level data to rank and score tasks based on multiple Criteria Indicators. For this to work:

The Main Indicator (used for Spotlight ranking) must have record collection enabled.

All Criteria Indicators (used to calculate Spotlight scores) must also have record collection enabled.

Without record collection, Spotlight cannot access the underlying task records needed for scoring. This requirement is explicitly emphasized in CAS–PA 2025 as a foundational setup for Spotlight functionality.

Incorrect:

A. The Main Indicator has a Threshold defined Incorrect. Thresholds are used for visual interpretation of scores (e.g., red/yellow/green zones), but they are not required for Snapshot or Spotlight evaluation. They do not influence record-level scoring.

B. The Main Indicator is a Daily Indicator Incorrect. The frequency of the Main Indicator (Daily, Weekly, etc.) is not a mandatory condition for Snapshot. What matters is record collection, not the time granularity.

C. The Main Indicator is a Formula Indicator Incorrect. The Main Indicator can be a standard Indicator or a Formula Indicator, but being a Formula Indicator is not a requirement for Snapshot. The key requirement is that record collection is enabled.

Correct:

A. Combine data from different applications Correct. Formula Indicators are commonly used to aggregate or compare data across multiple applications (e.g., incidents from ITSM and cases from HR). This allows unified KPI tracking across business units, a key use case emphasized in CAS–PA 2025.

C. Build predictive indicators based on historical performance Correct. While not full machine learning, Formula Indicators can be configured to project trends or flag anomalies based on historical scores. For example, comparing current performance to a trailing average or previous period helps build predictive insights.

D. Calculate ratios and percentages Correct. This is one of the most frequent uses of Formula Indicators. Examples include:

Resolution rate = Resolved incidents / Total incidents × 100

SLA compliance = Met SLAs / Total SLAs × 100 These calculations are foundational to KPI reporting and are explicitly covered in CAS–PA 2025.

Incorrect:

B. Generate a seasonal forecast based on a best-fit function Incorrect. ServiceNow Performance Analytics does not support statistical forecasting models like best-fit curves or seasonal decomposition within Formula Indicators. Advanced forecasting requires external tools or Predictive Intelligence, not native formula logic.

Correct:

A. Yes, by creating separate Breakdown Mappings for each Indicator Facts table. This is the correct approach in ServiceNow Performance Analytics. A single Breakdown (e.g., Assignment Group) can be reused across multiple Indicators, even if those Indicators are based on different Facts tables (e.g., incident, change_request). To do this:

You must create a separate Breakdown Mapping for each Facts table.

Each mapping links the Breakdown Source to the relevant field in the Indicator’s Facts table. This modular reuse of Breakdowns is a best practice emphasized in CAS–PA 2025 for maintaining consistency and reducing redundancy.

Incorrect:

B. No, you must create a separate Breakdown record for each Indicator Facts table. Incorrect. You do not need to duplicate Breakdown records. The Breakdown itself is reusable; only the mapping needs to be customized per Facts table.

C. Yes, but you must create a mapping script to define the relationship between the Indicator Facts tables and the Breakdown Source. Incorrect. A mapping script is optional, used only when complex logic is needed to associate Breakdown values. It is not mandatory for every Breakdown reuse across different tables.

D. Yes, as long as the Indicator field uses the same Breakdown source, no further configuration is required. Incorrect. Even if the Breakdown Source is the same, you must still define a Breakdown Mapping for each Facts table. The system needs to know how to link Breakdown values to each table’s records.

Correct:

D. Compare records This is the correct feature in KPI Details used to list records that have moved in, moved out, or remained between two selected periods. It enables users to:

Identify which records contributed to score changes

Analyze transitions (e.g., incidents resolved, tasks reassigned)

Support root cause analysis and performance tracking

This functionality is central to record-level analysis in Performance Analytics and is explicitly covered in CAS–PA 2025 as a key capability for data-driven decision-making.

Incorrect:

A. Show calendar Incorrect. The calendar view allows users to navigate and select time periods, but it does not list record transitions. It’s a date selection tool, not an analysis feature.

B. Filter Incorrect. Filters are used to narrow down displayed records or scores based on conditions (e.g., assignment group, priority), but they do not compare records across periods.

C. More options Incorrect. The “More options” menu may provide access to additional settings or actions, but it does not directly offer record comparison functionality.

Correct:

A. Configure Breakdown Matrix Exclusions in the Indicator definition Correct. This is a recommended method to control Breakdown data growth. By configuring Breakdown Matrix Exclusions, you can exclude specific Breakdown combinations that are irrelevant or unnecessary for score collection. This reduces the number of matrix entries generated and stored, helping optimize performance and storage.

D. Uncheck “Collect breakdown matrix“ in the Indicator definition Correct. If you don’t need matrix-style breakdown data (i.e., combinations of multiple breakdowns), you can disable matrix collection by unchecking this option. This prevents the system from generating and storing matrix scores, which can significantly reduce data volume.

Incorrect:

B. Exclude Breakdowns from historical collection jobs Incorrect. While you can configure collection jobs to limit historical data, this does not directly control Breakdown data growth. Breakdown scores are tied to Indicator configuration, not just job scope. Excluding Breakdowns from jobs may lead to incomplete data, not optimized storage.

C. Delete Breakdown scores from the Indicator scoresheet Incorrect. Manually deleting Breakdown scores is not a scalable or recommended method for managing data growth. It risks data integrity, breaks historical analysis, and does not prevent future overcollection.

Correct:

C. Sets the timezone that will be used for date functions in conditions

The Run as tz (timezone) setting in a Performance Analytics Data Collection configuration dictates the timezone used when evaluating date functions within the indicator‘s conditions or the script‘s calculations. For instance, if a condition uses a function like gs.beginningOfThisWeek(), the “beginning of this week“ is determined relative to the timezone specified in the Run as tz field, not the user‘s timezone or the system‘s timezone. This ensures consistent data collection regardless of where the system or the job runner is located.

Incorrect:

A. Reflects the timezone of the user running the job: The Run as tz is a specific configuration setting for the job itself, chosen by the admin when setting up the data collection. It does not automatically reflect or inherit the timezone of the user who last ran the job or the user in the “Run as“ field. The “Run as“ user primarily determines security/access permissions for the data collection, not the timezone for date calculations.

B. Sets the timezone for the job‘s schedule time: The time for when a scheduled job runs (e.g., 2:00 AM daily) is typically based on the system timezone (which is often GMT/UTC) unless a specific scheduler feature is used, but the Run as tz field is not used for this purpose. It only affects the evaluation of date conditions during the run.

D. Sets the timezone for score_start and score_end: The score_start and score_end times that define the time frame for which data is being collected are usually derived from the system timezone (UTC/GMT) for consistency within the database, and then manipulated by the data collector‘s logic. The Run as tz setting only influences the intermediate date functions in conditions, not the final start/end times stored with the scores.

Correct:

A. Analytics Hub: The Analytics Hub (or Analytics Center in newer releases/contexts) is the central place where users can view detailed performance information for a specific indicator. This detailed view includes the trend line, targets, and most importantly, the forecasted scores which appear as a dotted line extending into the future based on past behavior and a selected time series model (e.g., Linear, Seasonal).

D. Time series widgets: A Time series widget (like a Line or Area chart on a PA Dashboard) is the visualization component used to display an indicator‘s score over a period of time. When forecasting is enabled on the indicator, the widget automatically displays the historical scores as a solid line and the machine learning-derived future forecasted scores as a dotted or shaded area extending beyond the present date.

Incorrect:

B. KPI Signals: KPI Signals is a feature that uses machine learning to automatically detect and alert on anomalies or unusual variations in an indicator‘s score that deviate significantly from the expected historical pattern. It focuses on identifying problems or deviations in the past and present data, not on observing a general long-term forecast of future scores.

C. KPI Composer: KPI Composer is an application that is used for designing and documenting the structure and relationship of Key Performance Indicators (KPIs) and their corresponding breakdowns, sources, and targets in a hierarchical way (KPI Tree). It is a planning and documentation tool, and does not display or generate any live scores or forecasts.

Correct:

• C. Not enough information provided: This is the correct option because the number of database queries depends on the number of Indicator Sources, not the number of Indicators. While the job has two indicators, the crucial information that is missing is the number of Indicator Sources those two indicators are based on.
? If both Indicators share the same single Indicator Source, the database will be queried 30 times (1 query per source $\times$ 30 daily periods).
? If each Indicator uses a separate Indicator Source, the database will be queried 60 times (2 queries per period $\times$ 30 daily periods).
? Since the number of Indicator Sources is not explicitly given, the actual number of database queries cannot be determined.
Incorrect:

• A. 60: This would be the number of queries only if the two indicators were based on two different Indicator Sources (2 sources $\times$ 30 days = 60 queries). Since the question states they are based on the same Indicator Source, and this is a potential trick answer, 60 is an incorrect conclusion without clarification.
• B. 30: This would be the number of queries only if both indicators were based on the same single Indicator Source (1 source $\times$ 30 days = 30 queries). This is a strong possibility, but since the exact number of Indicator Sources is not explicitly stated in the scenario (it only says the indicators are based on the same Indicator Source), we cannot assume only one Source exists for the whole job execution.
• D. 1: This is incorrect. A single query (1) would only happen if the job collected data for just one period (e.g., Relative Start: 1 day ago, Relative End: 1 day ago) and used one Indicator Source. Since the job is configured to collect 30 periods of daily data ($30$ days ago to $1$ day ago), it will iterate and query the database for each of those 30 periods.
A crucial Performance Analytics principle is that a Data Collection Job queries the database once per unique Indicator Source for each collection period.

Correct:

A. PA Threshold Admin

Correct:

A. PA Threshold Admin (pa_threshold_admin): This role has the responsibility and capability to create and manage global thresholds for indicators. Global thresholds are visible on both the Analytics Hub and Performance Analytics widgets. The question asks who has the responsibility to create thresholds, making the dedicated administrative role for thresholds the most accurate answer for creating global/formal thresholds.

Incorrect:

B. PA Administrator (pa_admin): This role is the highest administrative role in Performance Analytics and can perform all tasks, including creating thresholds. However, the ServiceNow PA role structure has a more specific role (PA Threshold Admin) for this task, so PA Threshold Admin is considered the most correct or responsible role from an exam perspective, which focuses on the specific segregation of duties. The Admin role can do it, but is not the only one designated for this specific function.

C. PA Viewer (pa_viewer): This role is primarily for viewing indicators and dashboards. A PA Viewer, or any user who can view an indicator on the Analytics Hub, can create personal thresholds for their own view, but they cannot create global thresholds that are visible to all users or on shared widgets. The question implies the responsibility for creating the general or global thresholds.

D. PA Contributor (pa_contributor): This role is typically for users who manage indicator records, manually enter scores, or create content like targets. While often having more rights than a Viewer, it does not hold the specific, dedicated right to manage global thresholds, which belongs to the pa_threshold_admin role.

E. PA Data Collector: This is not a standard user role in ServiceNow Performance Analytics. The term refers to the scheduled job functionality that queries the database and collects scores. It is a system process, not a user role with responsibility for creating configuration records like thresholds.

Correct:

B. If you belong to report_group, you can share any report shared with you or with a group you belong to: This is a core capability of the report_group role. This role allows the user to manage and re-share reports that have been shared specifically with them (the user) or with any group they are a member of. This is how report sharing is decentralized to power users within groups.

E. If you belong to report_admin you can share any report with everyone or with Groups and Users: The report_admin role is the highest level of reporting access. It allows the user to manage, share, publish, and schedule all reports on the instance, regardless of who created them or how they are currently shared. This includes the ability to share with Everyone, specific Groups, or individual Users.

Incorrect:

A. If you belong to report_global, you can share any report shared with your group: This is incorrect. The function of the report_global role is to allow a user to re-share a report that is already shared with Everyone (globally). It does not grant the ability to re-share reports that were only shared with a group the user belongs to; that is the function of the report_group role.

C. If you belong to report_global, you can edit any report shared with you or with a group you belong to: This is incorrect. The report_global role is about sharing reports that are already public (Everyone sharing), not about editing them. The ability to edit reports shared with a group is not a primary function of this role, nor does it typically include edit rights to any report shared with the user or group.

D. If you belong to report_group, you can share any report shared with everyone: This is incorrect. The report_group role grants sharing permissions based on user/group access (reports shared with the user or their groups). It does not grant the ability to share reports that were shared with Everyone (globally) or all reports on the instance. The role that specifically deals with reports shared with Everyone is report_global.

Correct:

A. PA Data Collector: The primary function of the PA Data Collector role is to manage the foundational data aspects of Performance Analytics. This includes:

Creating, editing, and managing Indicator Sources and Breakdown Sources.

Creating and running Data Collection Jobs (scheduled and on-demand).

The role provides permissions to configure the collection of data but is specifically intended for users who are not responsible for the end-user presentation layer, meaning they do not create dashboards, widgets, or indicators.

Incorrect:

B. PA Viewer: This role is the most restrictive. The PA Viewer can only view existing Performance Analytics content (widgets and dashboards) that has been shared with them. They have no permissions to create, edit, or manage any configuration items, including data collection jobs or sources.

C. PA Contributor: This role allows users to manually add, edit, or delete scores on the Scoresheet for indicators they can access. This is primarily for data quality and correction tasks and does not include the responsibility for managing the data collection jobs themselves.

D. PA Power User: This is a higher-level role that typically includes the ability to create and configure Indicators, Breakdowns, and Widgets/Dashboards, in addition to sometimes possessing the permissions to manage data collection. The question asks for a role that manages data collection but does not create dashboards, which contradicts the responsibilities of a PA Power User, who is an end-to-end builder.

E. PA Target Admin: There is no standard role in Performance Analytics named “PA Target Admin.“ Target and Threshold configuration is generally part of the PA Admin or PA Power User responsibility.

Correct:

B. Default field: This setting determines which text field on the record (e.g., Short description, Description, or Comments) the Text Analytics widget will analyze to generate the word cloud.

C. Cutoff type: This display setting controls how the widget limits the words displayed. It can be set to either Percentage (to show words that meet a minimum percentage of the total count) or Fixed (to show words that meet a minimum count threshold).

E. Maximum number of words: This setting directly limits the total number of words that will be displayed in the word cloud visualization, regardless of their frequency, to keep the widget readable and focused.

Incorrect:

A. Show data labels: While many other PA chart widgets (like time series or breakdown widgets) have a “Show data labels“ option, this is not a standard, dedicated display setting for the Text Analytics Widget (Word Cloud). The size of the word itself serves as the visual representation of its frequency (the “label“).

D. Show comments: This is one of the fields that can be selected for the Text Analytics to analyze, which is configured via the Default field setting (Option B). It is not a separate display setting to show or hide all comments on the final word cloud visualization.

Correct:

A. Update a Dashboard: The PA Solutions Library allows you to update the pre-configured dashboards that came with an installed solution (Content Pack) when a new version of that content is released by ServiceNow.

B. Update a Report: Similar to dashboards, the library enables you to update reports that are part of an installed solution to leverage new features or bug fixes in a newer version.

C. Reinstall a Widget: If a widget that came with a solution has been accidentally modified, corrupted, or needs to be reverted to its original state, the library allows you to reinstall the specific widget or the entire solution to restore the out-of-the-box components.

Incorrect:

D. Design a new Dashboard: The Solutions Library is a management tool for pre-built content. The process of designing a new Dashboard is performed manually using the Dashboard editor and is a core function of the PA application, not the Solutions Library.

E. Design a new Widget: Similar to a new dashboard, designing a new Widget is an action performed manually within the Performance Analytics application or Analytics Hub. The Solutions Library manages the installation, update, and reinstallation of existing (ServiceNow-provided) content.

Correct:

A. Grant the metric admin role to the pa_admin user This is the correct approach to allow a user with the pa_admin role to create and edit Metric Definitions in ServiceNow. The metric_admin role is specifically required to manage Metric Framework configurations, including:

Creating new Metric Definitions

Editing existing Metric Definitions

Managing Metric Types and Sources

The pa_admin role alone does not grant access to Metric Definitions, as Metrics are part of a separate framework from Performance Analytics. CAS–PA 2025 emphasizes role-based access control, and this separation ensures that only authorized users can configure Metrics.

Incorrect:

B. Share the specific Metric Definition with the pa_admin Incorrect. Metric Definitions are not shared like records or dashboards. Access is controlled via roles, not record-level sharing. Without the metric_admin role, the user cannot create or edit Metric Definitions even if they can view them.

C. pa_admins already have rights to all metric tables Incorrect. The pa_admin role provides access to Performance Analytics features, but not to Metric Framework tables. These require explicit assignment of the metric_admin role.

D. Grant the admin role to the pa_admin user Incorrect. While the admin role would technically provide access, it is not best practice to grant full administrative privileges just to enable Metric Definition editing. CAS–PA 2025 promotes least privilege access, and the correct method is to assign the specific metric_admin role.

Correct:

D. Number of open problems not update in last 90 days: This measurement uses a condition based on a field‘s non-update within a relative time period (e.g., sys_updated_on NOT greater than 90 days ago). Performance Analytics Historical Data Collection works by looking at the current state of the records in the database for each historical date. An indicator that uses a “not updated“ condition is inherently problematic for historical collection because:

The condition is time-relative: A record that was not updated in the last 90 days on a historical date (e.g., January 1st) may have been updated between that historical date and today.

Inaccuracy: When the historical job runs today, it checks the record‘s current sys_updated_on date. If the record was updated after the historical date but is still open today, the historical collection will incorrectly exclude it from the “not updated“ count for the historical date. You cannot accurately determine a state based on a relative non-update condition for a date in the past. Indicators that depend on a record‘s current state based on a relative timeframe should be excluded from historical collection.

Incorrect:

A. Number of incidents resolved in time: This is a count of records where a specific event (resolution) occurred on a given day and met a condition (resolved in time). This type of indicator is based on a fixed, non-changing historical event (resolved_at date), making it suitable for accurate historical collection.

B. Number of new requests: This is a count of records created on a given day. The creation date is a static date field (sys_created_on) that never changes, making this indicator highly suitable for accurate historical data collection.

C. Summed age of open problems: This is a snapshot indicator that captures the total age of all currently open problem records. While the individual age of a record changes daily, the total summed age for all records that were open on the historical date can be accurately calculated. This indicator type is based on the difference between the open date and the collection date, and is one of the standard ways to measure backlog effort/debt. The historical job can correctly calculate the age for each record as of that historical date.

Correct:

C. Reference: This is a valid Interactive Filter type. The Reference filter is used to filter reports and widgets based on a field that references another table (e.g., filtering Incidents by Assignment Group or Assigned To). It typically provides a lookup or choice box populated with records from the referenced table.

D. Date: This is a valid Interactive Filter type. The Date filter allows users to filter dashboard data based on date fields (e.g., Created, Resolved, Due Date). It offers relative date ranges (e.g., Last 7 days, This Month) and fixed date ranges, providing powerful time-based filtering.

E. Choice list: This is a valid Interactive Filter type. The Choice List filter is used to filter based on fields with a predefined set of values, such as State, Priority, or Category. The filter is created from the field‘s available choices and can be displayed on the dashboard as a Dropdown, Radio Buttons, or Checkboxes.

Incorrect:

A. Groups and Users: While filtering by groups and users is common, Groups and Users is not a distinct, standalone Interactive Filter type in the way Reference, Date, and Choice List are. Group and User fields are filtered using the Reference Interactive Filter type, as both ‘Group‘ and ‘User‘ are simply fields that reference the sys_user_group and sys_user tables, respectively.

B. Sys id: The Sys id (System ID) is the unique identifier for a record in a table. It is not an end-user-facing Interactive Filter type provided out-of-the-box. Interactive Filters are designed to use user-friendly field types like Reference, Date, or Choice, not technical identifiers like the sys_id.

Correct:

C. Configured String fields from Indicator sources that have scores collected: Text Analytics in Performance Analytics is a targeted feature. It does not analyze text across the entire platform. Instead, an administrator configures specific String fields (such as Short description, Description, or Comments) on the tables used by Indicator Sources where Performance Analytics scores are collected. This ensures that the text being analyzed is directly relevant to the Key Performance Indicators (KPIs) being tracked and that the analysis is performed only on the data that has been collected by a PA job.

Incorrect:

A. All audit fields on all records: Audit fields (which track changes to records) are generally not relevant for Text Analytics, which focuses on the descriptive text content of records like incidents or cases. Furthermore, analyzing all records is not the focused approach of PA.

B. Short (less than 100 characters) String fields on all records: While short text fields like Short description are often configured for analysis, the analysis is not limited to an arbitrary character count like 100, and it is not performed on all records. The specific fields must be explicitly configured.

D. All String fields on all records: Analyzing all String fields on all records would be excessively resource-intensive and would pull in irrelevant data. Performance Analytics focuses on collected indicator scores and their associated records.

Correct:

B. How many tables have existing reports against them

D. The most common visualization in reporting

E. How many scores are added weekly

Incorrect:

A. The slowest running job

C. How many new users are using the Knowledge Base

Explanation

The Usage component within the Performance Analytics (PA) Admin Console provides administrators with an overview of how PA and Reporting features are being used on the instance.

Correct:

B. How many tables have existing reports against them: This is a key usage metric for Reporting. The Admin Console tracks report usage and can show which tables are being reported on, which indicates the overall health and adoption of the platform‘s analytics tools.

D. The most common visualization in reporting: The Admin Console tracks how users are interacting with the reporting and visualization tools. Knowing the most common visualization (e.g., bar chart, list, pie chart) helps administrators understand user preference and focus on optimizing those particular visualization types.

E. How many scores are added weekly: This is a core metric for Performance Analytics. The Usage component tracks the volume of collected data, such as how many scores are collected daily, weekly, or monthly, which is crucial for monitoring the health and performance of the PA data collector jobs.

Incorrect:

A. The slowest running job: While knowing the slowest running job is a critical administration task, this functionality is typically found in the Diagnostics or Job Log components of the Admin Console, which focuses on job performance and troubleshooting, not general usage.

C. How many new users are using the Knowledge Base: This is a very specific, application-level metric (for Knowledge Management) that would be tracked by a dedicated PA Indicator and displayed on a PA Dashboard or in the Analytics Hub, not a general usage statistic provided by the main PA Admin Console‘s Usage component.

Correct:

B. Any user with a role can create and share Responsive Dashboards

In ServiceNow, any user with a role (not just administrators or users with specific PA roles) has the ability to create a new personal dashboard. They can also share their owned dashboards with other users or groups. To view a Performance Analytics dashboard, a user typically needs the pa_viewer role or equivalent access, but the creation capability is broad for dashboards in general.

C. Responsive Dashboards are more customizable and perform better than Homepages

This is true. Responsive Dashboards are the modern, recommended reporting interface and offer significantly more flexibility and customization than the older Homepages, which had fixed layouts. They are also generally designed to perform better, especially in instances where widgets load data dynamically as the user scrolls, improving initial load times compared to Homepages.

D. Responsive Dashboards allow to drag and move Widgets in order to resize them

This is a key feature of Responsive Dashboards. Unlike the fixed layout columns of Homepages, users can drag and drop the dashboard widgets to rearrange them freely on the canvas, and they can also resize them by dragging the corners or edges, offering greater control over the visual presentation and density of information.

Incorrect:

A. Responsive Dashboards are slower than non-Responsive Dashboards

This statement is false. Responsive Dashboards are the current generation of dashboards in ServiceNow and are designed to be faster and more performant than the legacy Homepages (which are often referred to as non-Responsive). Their architecture, which includes features like lazy-loading widgets, is optimized for better performance.

E. Responsive Dashboards require that you apply a preconfigured layout

This statement is false. While Responsive Dashboards offer preconfigured layouts (like 4×4 or 3×3 grids) to help users quickly arrange widgets, they do not require them. Users are free to use the free-form canvas to place, size, and rearrange widgets entirely on their own using the drag-and-drop functionality, making this a flexible, optional feature, not a requirement.

Correct:

B. Personal

In ServiceNow Performance Analytics, a Personal target is created by an individual user for their own private use. These targets are visible only to the user who created them and, by design, cannot be shared with other users, groups, or roles. They are intended for individual tracking and goal setting.

Incorrect:

A. Global

Global targets are established at the administrative level for all users viewing an Indicator. These targets are explicitly shared with all users who have access to the Indicator and are often used to define a common organizational goal.

C. Threshold

Thresholds define a static range (e.g., green, yellow, red) for an Indicator score. Thresholds are part of the Indicator configuration and are shared with all users who can view the Indicator.

D. Trend

Trend targets are typically set to track if a score is improving or deteriorating compared to a previous period (e.g., “Trending up“ or “Trending down“). Trend targets are based on the Indicator‘s setup and are inherently shared with all users viewing the Indicator‘s score and its associated KPI Details.

E. Forecast

Forecast targets are generated by the system‘s predictive intelligence capabilities to project future performance. These projections are part of the detailed score view and are thus shared with all users who have access to view the Indicator and the Analytics Hub.

Correct:

A. Scores are retrieved from the collected Sys Ids of an Indicator

When you enable “Use Snapshot“ for a Spotlight Group, Performance Analytics uses the list of records (identified by their Sys IDs) that were collected when the associated Indicator score was generated (the snapshot). Instead of querying the live table, the Spotlight ranking and analysis are performed directly on this static, captured set of records. This ensures consistency between the score and the ranked records, and it often improves performance.

Incorrect:

B. Scores are retrieved from a database view

A database view can be used as a source for an Indicator‘s data collection, but enabling “Use Snapshot“ specifically means the system uses the collected Sys IDs from the moment the score was calculated, not the live database view. The snapshot is a static list of records, independent of the live view.

C. Scores are retrieved from business tables in real time

This is the opposite of the “Use Snapshot“ functionality. Retrieving scores and record details from the business tables in real time is how a traditional list or report would function. “Use Snapshot“ is a performance optimization that leverages the historical, static data collected at the time of the indicator job to ensure consistency and speed, not real-time data from the source table.

D. Scores are retrieved form an external source

Performance Analytics indicators can pull data from external sources using methods like IntegrationHub, but this is independent of the “Use Snapshot“ feature. “Use Snapshot“ controls how the records associated with a collected score are analyzed within the platform, not where the raw score data originates.

Correct:

A. Clicking on a record number

When the “Show Records“ option is enabled in the Analytics Hub (or KPI Details), it displays a list of the records (e.g., Incidents, Problems, etc.) that contributed to the current score. Clicking the number (the link to the record, like ‘INC0001001‘) in this list will open the form for that record, allowing the user to view and edit the record, provided they have the necessary security permissions (ACLs).

D. Clicking the Information icon next to the list entry

In the record list presented when “Show Records“ is active, the Information icon (often a small ‘i‘ in a circle or similar icon) is available next to each record entry. Clicking this icon typically opens a popup or quick view of the record, which usually includes an option to navigate to or edit the full record form. This is another direct way to access and modify the record from the KPI Details view.

Incorrect:

B. Clicking on a global target

A global target is a line or value displayed on the chart to represent a shared organizational goal. Clicking a target line or value does not open a record for editing; it is used to view or manage the target configuration itself, if the user has the appropriate edit rights.

C. Clicking on Add targets to breakdowns

The “Add targets to breakdowns“ option is used for configuring additional targets for specific breakdown elements. It is a setup and configuration action, not a mechanism for editing the underlying transactional records that are listed in the “Show Records“ view.

Correct:

B. Directions

The Directions column shows an icon indicating the desired trend for the score (e.g., a green up arrow for “Higher is Better“). This is a column that can be displayed in the Scorecard visualization within a Breakdown Analytics widget but is typically not a standard column in the main Analytics Hub view, which focuses more on the current score, change, trend, and forecast/target gap.

D. Frequency

The Frequency of collection (e.g., Daily, Weekly, Monthly) is a characteristic of the underlying Indicator itself, which dictates how often new scores are captured. While this information is crucial for understanding the data, it is not a column that is displayed on the list of breakdown elements in either the Analytics Hub or the Scorecard visualization of a Breakdown Analytics widget.

Incorrect:

A. Trend

The Trend value, typically shown as a change over time (e.g., a 30-day average), is a common column in both the Analytics Hub (often shown in the upper card) and the breakdown element list of the Scorecard visualization.

C. Gap

The Gap represents the difference between the current score and a defined Target (or Forecast). This is a crucial metric in Performance Analytics and is a common column in both the Analytics Hub and the breakdown element list of the Scorecard visualization.

E. Change

The Change column shows the difference between the current score and the previous score (or previous period). This is a fundamental PA metric and is a common column in both the Analytics Hub and the breakdown element list of the Scorecard visualization.

Correct:

D. Enable drilldown

The term Drilldown in ServiceNow visualizations refers to the ability to click a data point (like a bar on a chart or a score on a KPI widget) and be taken to a list of the records that comprise that score or data subset. To allow users to click a visualization and view the underlying data records within a workspace, the Enable drilldown option must be configured and activated for that specific widget or data visualization. The default drilldown action often directs the user to a filtered list view (Go to data view) or can be configured to navigate to a specific page or URL.

Incorrect:

A. New elements

New elements refers to adding new components like reports, widgets, or lists to a dashboard or workspace page. It is a step in the building process but does not specifically control the interactive feature of clicking a visualization to see its records.

B. Follow filters

Follow filters (or “Interactive Filters“) is a property that ensures a visualization‘s data is dynamically filtered when a user selects a value from a filter widget on the same dashboard or workspace. While essential for dynamic analytics, it controls input (how the visualization is filtered), not the output action of clicking the visualization itself to see records.

C. Activate breakdowns

Breakdowns are used to group or categorize indicator scores (e.g., by Assignment Group, Priority, etc.). Activating a breakdown makes the segmented data available for analysis, but it is a data configuration step, not the specific setting that enables the click-through action to view the records list.

Correct:

A. Average

The Average aggregate is the most appropriate choice because the user is asking for the mean Duration (the total time divided by the count) of the resolved incidents. In Performance Analytics, when applied to a duration field (like business_duration or duration), the Average aggregate calculates the average time value for all the records that meet the indicator‘s conditions. This directly computes the mean duration.

Incorrect:

B. Count

Count returns the total number of records (e.g., the number of resolved incidents). It does not perform any mathematical operation on the duration field and therefore cannot calculate the mean duration.

C. Distinct Average

The Distinct Average aggregate calculates the average of only the unique values in a specified field. While an average is part of the calculation, using ‘Distinct‘ on a duration field is generally not what is required for standard reporting, as most incidents will have a unique duration. Furthermore, the question asks for the mean duration of resolved incidents, which implies calculating the average across all resolved incidents, not just those with a unique duration value. The standard Average is the correct choice.

D. Sum

Sum returns the total accumulated value of a field across all records. Applying Sum to the Duration field would give the total combined duration of all resolved incidents (e.g., 500 hours). It does not divide by the number of incidents and therefore does not calculate the mean duration.

Correct:

C. Once or On Demand

Historical Collection jobs are designed to populate an Indicator‘s score history with data collected from before the initial daily collection began. Since the data you are collecting is static (it won‘t change as it‘s past data), this job only needs to be run once to fill the historical gap. Running it again is redundant and unnecessary. Alternatively, if a minor gap or correction is needed, it should be run On Demand (manually) for a specific date range, not on a scheduled recurring basis.

Incorrect:

A. Monthly

Setting a historical job to run Monthly is incorrect because historical data is static and should only be collected once. Scheduling it to run monthly would waste system resources by re-collecting the same old data repeatedly. Monthly is a common frequency for standard PA collections, but not for the initial historical run.

B. Daily

Setting a historical job to run Daily is incorrect for the same reasons as setting it to run monthly. Daily collection is the recommended frequency for regular (non-historical) Indicator score collection to track ongoing performance, but it‘s entirely inappropriate for a one-time historical data pull.

D. Weekly

Setting a historical job to run Weekly is incorrect. This, like Daily and Monthly, introduces unnecessary overhead by repeatedly collecting the same historical data that has already been captured. Weekly is a valid frequency for standard Indicator collection, but not for the initial historical backfill.

Correct:

B. The Facts table of the Breakdown Source is set to [pa_buckets]

To use a Bucket Group in an Automated Breakdown, the underlying data source for the Breakdown (the Breakdown Source) must be configured to reference the Bucket Group‘s data. This is achieved by setting the Facts table on the Breakdown Source record to [pa_buckets]. This table is where Performance Analytics stores the calculated bucket values, allowing the breakdown to segment scores based on those defined buckets (e.g., small, medium, large incidents).

Incorrect:

A. The Facts table of the Breakdown is set to [pa_buckets]

The Breakdown record itself does not have a “Facts table“ field. The facts table is an attribute of the Breakdown Source, which the Breakdown references. Therefore, this option incorrectly identifies the record where the configuration change needs to be made.

C. The Related list conditions of the Breakdown Source identify the Bucket Groups

The Related list conditions on the Breakdown Source are used to filter the records from the facts table (the records that contribute to the breakdown elements). They are used to apply standard record filtering, not to define or identify the existence of a bucket group.

D. The Default elements filter of the Breakdown specifies the Bucket Groups

The Default elements filter on a Breakdown is used to filter which breakdown elements (like a specific priority or assignment group) appear by default in the Analytics Hub. It is not the mechanism for configuring the breakdown to use a Bucket Group‘s logic; that configuration happens at the Breakdown Source level.

Correct:

B. PA Contributor

The PA Contributor role (pa_contributor) is specifically designed for users who need to view indicator scores (like a pa_viewer) but also need the ability to contribute to the data. This contribution primarily takes the form of accessing and modifying scoresheets for indicators to which they have been granted access. This allows managers or specific analysts to manually enter or adjust scores as part of the data governance process.

Incorrect:

A. PA Power User

The PA Power User role (pa_power_user) has broad administrative capabilities, including creating, modifying, and deleting indicators, breakdowns, and widgets. While a Power User can access and modify any scoresheet because they are administrators of the PA application, the PA Contributor is the most specific role defined for the purpose of accessing and modifying select scoresheets without having full administrative privileges.

C. PA Data Collector

The PA Data Collector role (pa_data_collector) is typically a service account role used by the scheduled job to run the collection process. It deals with automated data collection, not manual access or modification of scoresheets by a human user.

D. PA Viewer

The PA Viewer role (pa_viewer) grants the ability to view dashboards, widgets, and indicator scores, but it does not include the permissions to access or modify the underlying scoresheets. Their access is read-only for scores.

E. PA Target Admin

The PA Target Admin is a legacy or non-standard term. The relevant role is the Target User or having the pa_target_admin role (often inherited by pa_admin or pa_power_user). This role is primarily focused on creating and managing targets, not the direct, manual editing of indicator scores in a scoresheet.

Correct:
A. Sum only shows data for complete months, Sum+ includes partial months as well
In a time series, Sum aggregates scores only for fully completed periods (e.g., complete months). Sum+, on the other hand, calculates a running or cumulative sum, which includes both completed and partial periods, allowing trend analysis to reflect the total accumulated score up to the current point in time.

Incorrect:
B. Sum+ takes the current scores and adds a pre-configured value
This is incorrect because Sum+ does not involve adding a pre-configured static value. It calculates a cumulative total of the scores over the specified time series.

C. Sum+ only shows data for complete months, Sum includes partial months as well
This reverses the functionality. Sum+ includes partial months, while Sum aggregates only completed periods, so this statement is incorrect.

D. Sum takes the current scores and adds a pre-configured value
Sum simply aggregates the scores for the completed period and does not add any pre-configured value, making this statement incorrect.

Correct:
D. Enable the Show target property of the Time series widget
To display a Target on a Time series widget, the admin must enable the Show target property within the widget configuration. Once enabled, the widget will display the associated target line across the time-based data, helping users visually compare actual performance against the desired target over time.

Incorrect:
A. Click the Targets checkbox in the Display settings of the Time series widget
There is no generic “Targets” checkbox in the display settings for Time series widgets. The specific configuration required is the Show target property, not a display checkbox.

B. It is not possible to show Targets on a Time series widget
This is incorrect because Time series widgets do support showing Targets, as long as the appropriate property is enabled.

C. Add the Target to the Targets Related List of the Time series widget
Targets are not added through a related list on the widget. They are linked through the indicator, and the widget simply needs the Show target property enabled to display them.

Correct:
A. Users with the admin, pa_admin, or pa_kpi_signal_admin role without being a responsible user
To take actions on KPI Signals—such as resetting a baseline, acknowledging a signal, or dismissing it—a user must have specific elevated roles. The roles admin, pa_admin, or pa_kpi_signal_admin provide the necessary permissions to manage KPI Signals, even if the user is not assigned as a responsible user for that KPI. These roles have full control over KPI Signal behavior and administration.

Incorrect:
B. Responsible users without workspace access
Being a responsible user does not grant the permissions required to reset baselines or dismiss signals. Without the required PA administration roles and workspace access, responsible users are limited in actions they can take.

C. Users irrespective of their level of responsibility
Not all users can manage signals. KPI Signal actions require elevated PA roles, so permissions are not universal and cannot be performed by just any user.

D. Only users with the admin role
While admin users do have the required permissions, they are not the only ones who can perform these actions. The pa_admin and pa_kpi_signal_admin roles also provide this ability, making this option too restrictive and therefore incorrect.

Correct:
A. A calculated score based on the weighting of several conditions
Performance Analytics Spotlight provides a weighted scoring model used to evaluate and prioritize records (such as incidents or requests). The score is calculated based on multiple configured conditions, each assigned a weight. This helps users quickly identify which records need attention by generating a single prioritization score from several contributing factors.

Incorrect:
B. A visualization of an indicator score to display on a dashboard
This refers to standard PA widgets or visualizations, not Spotlight. Spotlight focuses on record-level prioritization, not chart or widget-based visualization of indicator trends.

C. A view of the record priority at the current time
While Spotlight can influence prioritization, it is not simply a direct view of priority. It calculates a dynamic score based on weighted conditions, not just the record’s existing priority field.

D. A view of the process data captured over a period of time
This describes time-series analytics in Performance Analytics. Spotlight does not analyze trends over time; it focuses on current record scoring and prioritization.

Correct:
D. Use reference currency
When monitoring monetary values in Performance Analytics, you should use the reference currency as the unit. This ensures consistent score collection, accurate currency conversion (when needed), and proper comparison across records that may involve different currencies. The reference currency maintains data integrity for financial indicators and guarantees uniform reporting across dashboards and analytics.

Incorrect:
A. Integer
Money values are not collected as integers because they often include decimals (for example, 199.95). Using Integer would lead to inaccurate score storage and rounding issues, making it unsuitable for currency-based indicators.

B. Currency FX
Currency FX refers to foreign exchange rate data, not a unit for indicator collection. It is not used as the unit for a monetary indicator but may be involved behind the scenes for conversion when reference currency is applied.

C. $
Using a symbol such as “$“ is not a valid unit choice in Performance Analytics for score collection. Currency symbols differ by region and are only for display. They do not ensure accurate, standardized financial tracking across data sources the way reference currency does.

Correct:
D. Text Analytics Phrases
Text Analytics Phrases allow Performance Analytics to identify and track multi-word terms or phrases within textual fields, such as Incident Descriptions. By configuring a phrase like “password reset,“ PA can trend its occurrences over time, providing insights into common issues or patterns. This is essential for analyzing recurring events that span multiple words rather than single keywords.

Incorrect:
A. Text Analytics Keywords
Keywords track individual words, not multi-word phrases. While useful for single-term analysis, keywords cannot accurately trend occurrences of combined terms like “password reset.“

B. Text Index Stop Words
Stop Words are common words (e.g., “the,“ “and“) that are excluded from indexing to improve performance and relevance. They do not track trends or occurrences of phrases.

C. Text Analytics Stop Words
Similar to Text Index Stop Words, these are used to ignore irrelevant words during text analysis. They cannot be used to trend specific phrases or meaningful terms in Incident Descriptions.

Correct:
B. Drives outcomes
A leading indicator influences or predicts future outcomes. It helps identify actions that can affect results, allowing organizations to take proactive measures before the outcome occurs.

C. Easy to influence
Leading indicators are typically actionable and controllable. Teams can influence them through specific interventions or process improvements, unlike lagging indicators, which only report results after the fact.

D. Input oriented
Leading indicators are focused on inputs or activities that contribute to an outcome. They track processes, behaviors, or early signals that precede results, enabling proactive management.

Incorrect:
A. Usually an average
This is not a defining characteristic of leading indicators. A leading indicator may be an average, a count, or any measurable input, but averaging is not what makes it “leading.”

E. Measure outcomes
Measuring outcomes is the role of lagging indicators, not leading indicators. Lagging indicators report on the results of actions, whereas leading indicators predict or influence those results.

Correct:
B. By groups and users AND By role
Access to the Indicator Analytics Hub can be controlled both by assigning specific roles and by granting access to individual users or groups. This dual approach allows administrators to manage broad role-based permissions while also tailoring access for particular teams or users, ensuring that only authorized personnel can view or interact with Analytics Hub content.

Incorrect:
A. By configuring “report on“ ACL for the Facts table
Configuring ACLs on the Facts table controls access to the underlying data but does not directly manage access to the Analytics Hub interface itself. Hub access is controlled separately through roles and group/user assignments.

C. By group only
Limiting access by group alone is insufficient because PA also supports role-based access. Users with appropriate roles may need Hub access even if they are not part of a specific group.

D. By role only
Role-only configuration ignores user or group-specific access requirements. The system allows flexibility to combine roles with group/user assignments, so role-only control is not sufficient for full Hub access management.

Correct:
C. Additional conditions are used to narrow the results obtained from the already processed Indicator Source
Additional conditions are applied after the main Indicator Source has been processed. They act as a filter to refine or limit the results, ensuring that only records meeting the extra criteria are counted in the indicator. This allows for more precise reporting without modifying the original Indicator Source.

Incorrect:
A. Additional conditions are not available on Indicators. All conditions are defined in the Indicator Source
This is incorrect because Indicators do support Additional conditions, which are separate from the main Indicator Source conditions. They provide a flexible way to further filter data.

B. Additional conditions are evaluated at the same time as the Indicator Source conditions
This is incorrect because Additional conditions are applied after the Indicator Source conditions, not simultaneously. They refine the already processed results rather than being part of the initial data query.

D. Additional conditions override Source conditions
Additional conditions do not override the Source conditions; they narrow the dataset, working in conjunction with the original conditions rather than replacing them.

Correct:
B. Information around governance and high-level overview of process indicators to make better informed decisions
Executives require high-level, summary information that reflects the overall health and performance of the organization. They focus on governance, trends, and key process indicators to make strategic decisions. Performance Analytics provides dashboards and indicators tailored to these needs, allowing executives to quickly understand business performance and identify areas requiring attention.

Incorrect:
A. Relevant targeted information that would help make the right decisions quickly and result in more efficient and better service
This describes the needs of operational managers or process owners, not executives. They require actionable, task-level insights rather than high-level governance metrics.

C. Status and quality information about their submitted requests and the services they use
This aligns with end-user or consumer needs, not executives. It focuses on personal service requests rather than organizational performance metrics.

D. Information that will help better understand what drives quality and cost of Service Delivery
This is more relevant to process improvement teams or operational management, who analyze drivers of service quality and cost. Executives focus on summarized performance and governance indicators rather than operational detail.

Correct:
A. Count
The Count aggregation calculates the total number of records meeting the indicator conditions. It is commonly used to aggregate multiple elements into a single KPI score, representing the volume of relevant items.

B. Sum
The Sum aggregation adds up values from multiple elements, allowing the calculation of a total score for a KPI. It is useful when measuring cumulative metrics, such as total cost, hours, or incidents.

C. Minimum
The Minimum aggregation identifies the lowest value among multiple elements. It can be used to generate an aggregated KPI score where the lowest value is critical, such as minimum response time or lowest SLA compliance.

Incorrect:
D. Average
While Average provides a summary metric, it does not directly facilitate aggregation of multiple elements in the same way Count, Sum, or Minimum do for KPI score calculations. Average is derived from the underlying elements rather than being a primary aggregation method for score calculation in KPI Details.

Correct:
A. Multiple Indicators can use the same Indicator Source
Automated Indicators can share a single Indicator Source. This allows different indicators to measure different aspects or apply different aggregations to the same underlying data without duplicating the data source.

D. Script can be used to calculate the Indicator scores
Scripts can be added to Automated Indicators to customize or extend the score calculation beyond standard aggregations. This enables complex or conditional scoring logic to be applied automatically.

E. Breakdowns can be applied for further classification
Breakdowns can be applied to Automated Indicators to categorize or segment the indicator data by fields such as assignment group, location, or category. This allows detailed analysis within the Analytics Hub or dashboards.

Incorrect:
B. One automated Indicator can act as the Indicator Source of another
Automated Indicators cannot serve as an Indicator Source. Indicators are calculated from Indicator Sources (tables, scripts, or manual inputs), but they themselves are not used as sources for other indicators.

C. Only users with the pa_data_collector role can create Automated Indicators
Creating Automated Indicators requires PA Admin or PA Power User roles, not just pa_data_collector. The pa_data_collector role is primarily for running data collection jobs, not creating indicators.

Correct:
C. Yes. The formula returns the Monthly running SUM of Indicator scores
The SUM+ operator in a Formula Indicator calculates the cumulative or running sum over the specified time series, in this case, “By month.” This syntax is valid and allows the formula to divide the current score by the cumulative monthly total, providing a meaningful aggregated value for trend analysis.

Incorrect:
A. Yes. The formula divides the current score by the monthly SUM of scores
This is partially correct but not precise. The SUM+ operator calculates a running cumulative sum, not just a simple monthly sum. Therefore, this description does not fully capture the functionality.

B. No. By Month SUM+ is not a valid Time series
This is incorrect because By month SUM+ is a valid time series syntax in Performance Analytics Formula Indicators. It specifies a monthly aggregation with cumulative addition.

D. No. The formula attempts illegal division
This is incorrect because dividing by a cumulative sum is legal in Formula Indicators, and the syntax does not violate any rules of PA formula construction.

Correct:
D. Visible to Everyone, Visible by All Roles is False
By default, new Indicators in Performance Analytics are visible to all users, but do not require a specific role to access. The “Visible by All Roles“ option is set to false, meaning that role-based restrictions are not applied unless explicitly configured. This default ensures broad visibility while allowing administrators to later restrict access if needed.

Incorrect:
A. Visible to Just Me, Visible by All Roles is False, role required is pa.admin
This is incorrect because the default setting is not restricted to just the creator (“Just Me”), and no role such as pa.admin is required by default.

B. Visible to Everyone, Visible by All Roles is False, role required is pa_admin
Although visibility to everyone is correct, the default does not require the pa_admin role. Role assignment is optional and must be configured manually.

C. Visible to Just Me, Visible by All Roles is False
This is incorrect because new Indicators are not restricted to the creator. By default, they are accessible to all users, not just the person who created the indicator.

Correct:
A. The data to evaluate and the weight threshold needed
A Spotlight group defines which records or data elements to evaluate and the weight thresholds required for scoring. Each group contains criteria that determine how a record contributes to the overall Spotlight score, and the threshold specifies the minimum weight for a record to be flagged or prioritized.

Incorrect:
B. The data to evaluate and the weight target needed
This is incorrect because Spotlight uses weight thresholds, not targets, to determine which records meet the criteria for attention. Targets are a separate concept in Performance Analytics.

C. The score of a record as the total weight from all applicable criteria
While the Spotlight score of a record is based on weights, the Spotlight group itself does not define the total score, only the criteria and thresholds used to calculate it.

D. The total score from all applicable criteria
This describes the resulting score, not the Spotlight group definition. The group defines inputs and thresholds, not the final aggregated score.

Correct:
A. Manual
This filter allows the Analytics Hub list to show only Manual Indicators. Since some indicators are sourced from data collection and others from manual input, filtering for Manual Indicators is a supported option in the list settings.

C. With a target
This filter allows users to display only indicators that have an associated Target. It is useful when focusing on KPIs that are being actively measured against a defined performance goal.

Incorrect:
B. Target hit
This is not a valid filter in the Analytics Hub list settings. “Target hit” refers to an outcome or result, not a filter that can be applied from the list settings gear.

D. With a threshold
Although indicators can have thresholds, this option is not available as a list filter in the Analytics Hub settings. Threshold configurations are managed separately.

E. Breakdown elements
Breakdown elements are not filters at the list settings gear. They are used for segmenting indicator data but cannot be applied as a filter from the list view controls.

Correct:
A. Relevant targeted information that would help make the right decisions quickly and result in more efficient and better service
Front Line Workers need actionable, task-focused information that helps them respond efficiently and effectively to incidents, requests, or operational tasks. Performance Analytics provides targeted dashboards and indicators that enable workers to prioritize their work, reduce resolution time, and improve service delivery.

Incorrect:
B. Status and quality information about their submitted requests and the services they use
This describes end-user or consumer needs, not front line workers. End-users are interested in the progress or quality of their own requests rather than operational performance metrics.

C. Information that will help better understand what drives quality and cost of Service Delivery
This is more relevant for process improvement teams or operational managers, who analyze drivers of service quality and cost to optimize processes.

D. Information around governance and high-level overview of process indicators to make better informed decisions
This aligns with executive-level needs, focusing on strategic governance and high-level performance trends rather than actionable insights for front line workers.

Correct:
A. score_end
The variable score_end represents the end value of the indicator score for the current data collection period. It can be used in scripts to calculate or manipulate scores dynamically within a Performance Analytics script.

B. score_start
The variable score_start represents the start value of the indicator score for the current data collection period. It is commonly used in scripts to reference the initial score or to calculate differences, growth, or trends during the collection period.

Incorrect:
C. collection_job_end
This is not a standard variable available for use within PA scripts. The PA scripting context specifically uses score_start and score_end to manipulate indicator data.

D. collection_job_start
Similar to collection_job_end, this is not a recognized variable in PA scripting. Scripts do not directly access job start times through this variable.

E. sys_created_on
While sys_created_on exists as a field in tables, it is not automatically provided as a variable in PA scripts for indicator calculation. Scripts work with the score context rather than individual record metadata unless explicitly queried.

Correct:
B. Number of Performance Analytics scores stored
The “Inserts“ value in a Job Log record represents the total number of PA scores that were created and stored during the execution of a data collection job. It indicates how many individual indicator scores were successfully added to the database for reporting and analytics purposes.

Incorrect:
A. Number of Indicator Source records examined
This is incorrect because “Inserts“ does not reflect the number of records evaluated in the Indicator Source. It only counts the scores actually stored.

C. The sum of the stored scores
This is incorrect because “Inserts“ is a count of stored scores, not a sum of their numeric values.

D. The number of incidents inserted into the instance yesterday
This is unrelated to PA Job Logs. “Inserts“ does not count incident records; it strictly counts Performance Analytics scores generated during the collection job.

Correct:
B. By adding ‘Incident->Assignment group’ to the Related List Conditions
To filter the Groups Breakdown to only include groups that have been assigned incidents, you use the Related List Conditions on the Elements Filter. Adding Incident ? Assignment group ensures that only groups linked to at least one incident record are included in the breakdown, making the indicator data relevant and accurate.

Incorrect:
A. By adding ‘itil’ to the Roles necessary to see the filter
This controls who can view the filter, not which groups are included. It does not filter the groups based on incident assignments.

C. By adding ‘itil’ type to the ‘Conditions’
Adding a type such as ‘itil’ does not restrict the groups to only those with assigned incidents. This is unrelated to the actual filtering of breakdown elements.

D. By selecting ‘Incident [incident]’ for the Facts table
Selecting the Facts table defines the source data, not the filtering logic for the Breakdown. Without using the Related List Conditions, all groups would be included regardless of whether they had incidents.

Correct:
B. 1 > 4 > 2 > 5 > 3
The correct sequence is:

Develop Parsing Script – Create the script that defines how data will be parsed for the breakdown.

New Breakdown with Script Map – Create the Breakdown and link it to the parsing script.

Create Breakdown Relation – Establish relationships between the breakdown and indicators or facts.

Collect for the new Breakdown – Run a data collection job to populate the breakdown with data.

Verify Analytics Hub – Check the Analytics Hub to confirm that the breakdown data appears correctly.

Incorrect:
A. 4 > 1 > 5 > 2 > 3
This sequence attempts to create the Breakdown before developing the parsing script, which is incorrect because the script must exist first.

C. 1 > 4 > 5 > 2 > 3
This collects data before creating the Breakdown Relation, which will result in incomplete or incorrect data mapping.

D. 4 > 1 > 2 > 5 > 3
Again, the Breakdown is created before the parsing script, which is not valid. The script must be developed first to define how the Breakdown will process data.

Correct:
A. Bars Correct. In UI Builder, Bar visualizations support multiple data sources of the same type. This allows you to compare values across different indicators or breakdowns side-by-side in a single chart. It’s commonly used for stacked or grouped bar charts where each bar segment represents a different source.

B. Time Series Correct. Time Series visualizations (e.g., line or spline charts) also support multiple data sources. This enables you to plot trends from several indicators over time on the same graph, making it ideal for comparative performance tracking (e.g., incidents vs. requests over months).

Incorrect:
C. Pie and donuts Incorrect. Pie and donut charts are limited to a single data source. They are designed to show proportional distributions from one indicator or report, not comparative data across multiple sources.

D. Single Score Incorrect. Single Score widgets display only one value from a single data source. They are used for highlighting key metrics (e.g., total open incidents) and do not support multiple sources.

Correct:
C. A dedicated one This is the recommended best practice for running Data Collection jobs in ServiceNow Performance Analytics. Using a dedicated service account ensures:

Audit clarity: All data collection activities are traceable to a single, purpose-specific account.

Security: Limits exposure of privileged roles and avoids using personal or admin accounts unnecessarily.

Stability: Prevents disruptions if a personal account is deactivated or modified. This aligns with CAS–PA 2025 guidance for maintaining clean, secure, and maintainable Performance Analytics configurations.

Incorrect
A. Your own Incorrect. Using a personal account for data collection is not recommended. It introduces risks such as:

Loss of functionality if the account is disabled.

Lack of audit separation between user actions and system jobs.

Potential permission mismatches over time.

B. System Administrator Incorrect. While technically possible, using the System Administrator account is discouraged. It has broad privileges, and running automated jobs under this account can:

Obscure audit trails.

Introduce unnecessary security risks.

Violate least privilege principles.

D. maint Incorrect. The maint account is a default system account used for platform maintenance tasks. It is not intended for Performance Analytics data collection and may lack the necessary configuration or visibility for PA-specific operations.

Correct:
D. Daily only This is the correct answer. When an Indicator Source is set to Daily, the only valid Indicator Frequency that can be used is Daily. This is because the Indicator Source defines the granularity of available data, and Indicators must match that frequency exactly to collect scores. In CAS–PA 2025, this is a critical configuration rule: Indicator Frequency must match the Valid for Frequency of the Indicator Source, and no aggregation or extrapolation is allowed across mismatched frequencies.

Incorrect:
A. Daily, Weekly, Monthly, Yearly Incorrect. This implies that you can use higher-level frequencies with a Daily source, which is not allowed. Indicators must match the source frequency exactly, not approximately.

B. Daily, Weekly, Monthly Incorrect. Same issue as above—Weekly and Monthly are not valid if the source is Daily. Only Daily Indicators can collect from a Daily source.

C. Daily, Weekly, Monthly
Incorrect. This is a duplicate of option B and remains incorrect for the same reason.

Correct:

B. By selecting the ‘New’ button on the ‘Breakdowns’ related list This is the correct method for creating and associating Breakdowns on the Breakdown Source form in ServiceNow Performance Analytics. The Breakdowns related list appears at the bottom of the Breakdown Source form, and clicking ‘New’ allows you to:

Define a new Breakdown (e.g., Assignment Group, Category)

Link it directly to the Breakdown Source

Configure its type (manual, mapping, or data-driven)

This is the standard and recommended approach covered in CAS–PA 2025.

Incorrect:

A. By adding multiple ‘Facts tables’ under the Source tab Incorrect. The Facts Table defines the base table for the Breakdown Source (e.g., incident, task), but you do not add multiple facts tables to create Breakdowns. Only one Facts Table is allowed per Breakdown Source.

C. From the ‘Create Breakdowns’ related link Incorrect. There is no ‘Create Breakdowns’ related link on the Breakdown Source form. Breakdowns are managed via the Breakdowns related list, not through a separate link.

D. From the ‘Additional actions’ menu Incorrect. The ‘Additional actions’ menu (gear icon or context menu) may offer form-level actions, but it does not provide a direct method to create or associate Breakdowns. The correct workflow is through the Breakdowns related list.

Correct:

B. [[Number of new changes > Risk – High]] / [[Number of new changes]] * 100 This is the correct formula expression for calculating the percentage of high-risk new changes using a Formula Indicator in ServiceNow Performance Analytics. It works as follows:

[[Number of new changes > Risk – High]] counts only the new changes where Risk = High.

[[Number of new changes]] counts all new changes.

The division gives the proportion, and multiplying by 100 converts it to a percentage.

This syntax is valid and aligns with CAS–PA 2025 best practices for filtering within Formula Indicators using breakdown-like logic.

Incorrect:

A. [[Number of new changes.risk = high]] / {{Number of new changes}} * 100 Incorrect. This syntax is invalid for Formula Indicators. You cannot use dot notation (.risk = high) inside the double brackets, and {{…}} is not a recognized syntax for referencing Indicators in formulas.

C. [[Number of new changes]] / [[Number of new changes]] * 100 Incorrect. This expression calculates 100%, regardless of risk level, because it divides the same value by itself. It does not filter for high-risk changes, so it fails to meet the intent of the question.

D. [[Number of new changes]] / {{Number of new changes}} * 100 Incorrect. This mixes two incompatible syntaxes ([[…]] and {{…}}), which is not allowed in Formula Indicator expressions. It also lacks any filtering for high-risk changes.

Correct Answer
C. By colour-coding This is the correct method used in KPI Details to show favourability—whether an increase or decrease in scores is considered positive or negative, based on the direction of the indicator. For example:

If an increase in score is favorable (e.g., resolution rate), it may be shown in green.

If a decrease is favorable (e.g., incident backlog), that decrease may also be shown in green, depending on the indicator’s configured direction.

This color-coded feedback helps users quickly interpret performance trends and aligns with CAS–PA 2025 best practices for visual score interpretation.

? Incorrect Answers
A. By different shapes Incorrect. KPI Details do not use shapes to indicate favourability. Shapes are not part of the visual feedback mechanism in Performance Analytics.

B. By different sizes Incorrect. Size variations are not used to reflect score favourability. Widget or chart size may vary for layout purposes, but it does not convey performance direction.

D. By naming convention Incorrect. While naming conventions may help identify indicators, they do not dynamically reflect favourability. The system relies on visual cues, not names, to show performance direction.

E. By different patterns Incorrect. Patterns (e.g., stripes, dots) are not used in KPI Details to indicate score favourability. Only color-coding is used for this purpose.

Correct:

A. 4 > 1 > 6 > 5 > 3 > 2 This is the correct sequence for setting up Breakdowns in ServiceNow Performance Analytics, as per CAS–PA 2025 best practices:

Create Breakdown Source Define the source table and logic for generating breakdown values (e.g., Assignment Group, Category).

Create Breakdown Create the Breakdown record and link it to the Breakdown Source.

Create Mapping Script if required If the Breakdown Source requires custom logic to map values to the indicator’s fact table, create a mapping script.

Create Breakdown Mapping to indicator fact table Establish the mapping between Breakdown values and the indicator’s fact table (e.g., incident, task).

Add Breakdown to indicator Associate the Breakdown with the relevant Indicator to enable segmented score collection.

Collect data and visualize Run data collection jobs and visualize scores using widgets and dashboards.

This sequence ensures that all dependencies are correctly configured before data collection begins.

Incorrect:

B. 1 > 4 > 6 > 5 > 3 > 2 Incorrect. You must create the Breakdown Source first before creating the Breakdown. This option reverses that order.

C. 1 > 4 > 3 > 5 > 6 > 2 Incorrect. It places Add Breakdown to indicator before Mapping, which is invalid. You must map the Breakdown to the fact table before linking it to the Indicator.

D. 1 > 4 > 5 > 6 > 3 > 2 Incorrect. It starts with Breakdown creation before the Breakdown Source, and reverses the order of mapping and scripting. Mapping scripts must be created before mapping if needed.

Correct:

B. Defines the Facts table field used to map the Breakdown to Indicators This is the correct purpose of the Field in Breakdown Mapping configuration. In ServiceNow Performance Analytics, when you configure a Breakdown Mapping, the Field specifies the column in the Facts table (e.g., incident.assignment_group) that will be used to link Breakdown values to the records being scored by the Indicator. This mapping ensures that scores can be segmented correctly by Breakdown (e.g., by Assignment Group, Category, Location), which is essential for enabling filtered views and targeted analysis.

Incorrect:

A. Provides filtering of the Breakdown Elements Incorrect. Filtering Breakdown Elements is handled through Breakdown Source filters, not the Field in Breakdown Mapping. The Field is used for mapping, not filtering.

C. Provides security for the Breakdown values based on user permissions Incorrect. Security and access control for Breakdown values are managed through roles, ACLs, and dashboard sharing, not through the Field in Breakdown Mapping.

D. Identifies the field that contains a unique value for every record in the Facts table Incorrect. The Field in Breakdown Mapping does not require uniqueness. It simply identifies the attribute used to associate Breakdown values with records. Uniqueness is not a requirement for this mapping.

Correct:

D. Playbook This is the correct feature in ServiceNow Workspace that enables agents to execute business process workflows in a simple, task-oriented view. A Playbook provides a guided, step-by-step interface that helps agents follow predefined processes (e.g., onboarding, incident resolution, change management) with clarity and consistency. It’s especially useful for complex workflows that require multiple actions, approvals, or branching logic. CAS–PA 2025 emphasizes Playbooks as a key tool for streamlining agent productivity and ensuring process adherence.

Incorrect:

A. Agent assistance Incorrect. While Agent Assist provides recommendations and contextual help (e.g., knowledge articles, similar cases), it does not guide agents through workflows. It’s a support tool, not a process execution framework.

B. Integrated communication channels Incorrect. These enable agents to communicate across platforms (e.g., chat, voice, messaging), but they do not structure or guide workflows. They support collaboration, not task execution.

C. Integrated email client Incorrect. The email client allows agents to send and receive emails within Workspace, but it does not facilitate workflow execution. It’s a communication tool, not a process guide.